Code updates

pints/_abc/__init__.py

@@ -16,10 +16,9 @@ class ABCSampler(pints.Loggable, pints.TunableMethod):
     :class:`pints.TunableMethod` interfaces.
     """
 
+    @classmethod
     def name(self):
-        """
-        Returns this method's full name.
-        """
+        """ Returns this method's full name. """
         raise NotImplementedError
 
     def ask(self):

pints/_abc/_abc_rejection.py

@@ -46,6 +46,7 @@ def __init__(self, log_prior):
         self._xs = None
         self._ready_for_tell = False
 
+    @classmethod
     def name(self):
         """ See :meth:`pints.ABCSampler.name()`. """
         return 'Rejection ABC'

pints/_abc/_abc_smc.py

@@ -106,6 +106,7 @@ def __init__(self, log_prior, perturbation_kernel=None,
             raise ValueError('Provided perturbation kernel must be an instance'
                   ' of pints.LogPrior')
 
+    @classmethod
     def name(self):
         """ See :meth:`pints.ABCSampler.name()`. """
         return 'ABC-SMC'

pints/_boundaries.py

@@ -129,7 +129,7 @@ class RectangularBoundaries(Boundaries):
         The corresponding upper boundaries
     """
     def __init__(self, lower, upper):
-        super(RectangularBoundaries, self).__init__()
+        super().__init__()
 
         # Convert to shape (n,) vectors, copy to ensure they remain unchanged
         self._lower = pints.vector(lower)
@@ -205,7 +205,7 @@ class LogPDFBoundaries(Boundaries):
         within bounds. Anything _above_ the threshold counts as within bounds.
     """
     def __init__(self, log_pdf, threshold=-np.inf):
-        super(LogPDFBoundaries, self).__init__()
+        super().__init__()
 
         # Check log pdf
         if not isinstance(log_pdf, pints.LogPDF):

pints/_core.py

@@ -20,7 +20,7 @@ class ForwardModel(object):
     """
 
     def __init__(self):
-        super(ForwardModel, self).__init__()
+        super().__init__()
 
     def n_parameters(self):
         """
@@ -67,7 +67,7 @@ class ForwardModelS1(ForwardModel):
     """
 
     def __init__(self):
-        super(ForwardModelS1, self).__init__()
+        super().__init__()
 
     def simulateS1(self, parameters, times):
         """

pints/_error_measures.py

@@ -54,7 +54,7 @@ class ProblemErrorMeasure(ErrorMeasure):
     :class:`multi-output<pints.MultiOutputProblem>` problems.
     """
     def __init__(self, problem):
-        super(ProblemErrorMeasure, self).__init__()
+        super().__init__()
         self._problem = problem
         self._times = problem.times()
         self._values = problem.values()
@@ -96,7 +96,7 @@ class MeanSquaredError(ProblemErrorMeasure):
 
     """
     def __init__(self, problem, weights=None):
-        super(MeanSquaredError, self).__init__(problem)
+        super().__init__(problem)
         self._ninv = 1.0 / np.prod(self._values.shape)
 
         if weights is None:
@@ -147,7 +147,7 @@ class NormalisedRootMeanSquaredError(ProblemErrorMeasure):
         A :class:`pints.SingleOutputProblem`.
     """
     def __init__(self, problem):
-        super(NormalisedRootMeanSquaredError, self).__init__(problem)
+        super().__init__(problem)
 
         if not isinstance(problem, pints.SingleOutputProblem):
             raise ValueError(
@@ -174,7 +174,7 @@ class ProbabilityBasedError(ErrorMeasure):
         The LogPDF to base this error on.
     """
     def __init__(self, log_pdf):
-        super(ProbabilityBasedError, self).__init__()
+        super().__init__()
         if not isinstance(log_pdf, pints.LogPDF):
             raise ValueError(
                 'Given log_pdf must be an instance of pints.LogPDF.')
@@ -216,7 +216,7 @@ class RootMeanSquaredError(ProblemErrorMeasure):
         A :class:`pints.SingleOutputProblem`.
     """
     def __init__(self, problem):
-        super(RootMeanSquaredError, self).__init__(problem)
+        super().__init__(problem)
 
         if not isinstance(problem, pints.SingleOutputProblem):
             raise ValueError(
@@ -271,7 +271,7 @@ class SumOfErrors(ErrorMeasure):
 
     """
     def __init__(self, error_measures, weights=None):
-        super(SumOfErrors, self).__init__()
+        super().__init__()
 
         # Check input arguments
         if len(error_measures) < 1:
@@ -360,7 +360,7 @@ class SumOfSquaresError(ProblemErrorMeasure):
 
     """
     def __init__(self, problem, weights=None):
-        super(SumOfSquaresError, self).__init__(problem)
+        super().__init__(problem)
 
         if weights is None:
             weights = [1] * self._n_outputs

pints/_evaluation.py

@@ -193,7 +193,7 @@ def __init__(
             max_tasks_per_worker=500,
             n_numpy_threads=1,
             args=None):
-        super(ParallelEvaluator, self).__init__(function, args)
+        super().__init__(function, args)
 
         # Determine number of workers
         if n_workers is None:
@@ -437,7 +437,7 @@ class MultiSequentialEvaluator(Evaluator):
         ``f(x, *args)``.
     """
     def __init__(self, functions, args=None):
-        super(MultiSequentialEvaluator, self).__init__(functions[0], args)
+        super().__init__(functions[0], args)
 
         # Check functions
         for function in functions:
@@ -473,7 +473,7 @@ class SequentialEvaluator(Evaluator):
         specified, ``f`` will be called as ``f(x, *args)``.
     """
     def __init__(self, function, args=None):
-        super(SequentialEvaluator, self).__init__(function, args)
+        super().__init__(function, args)
 
     def _evaluate(self, positions):
         scores = [0] * len(positions)
@@ -530,7 +530,7 @@ class _Worker(multiprocessing.Process):
     def __init__(
             self, function, args, tasks, results, max_tasks, max_threads,
             errors, error):
-        super(_Worker, self).__init__()
+        super().__init__()
         self.daemon = True
         self._function = function
         self._args = args

pints/_log_likelihoods.py

@@ -69,7 +69,7 @@ class AR1LogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem):
-        super(AR1LogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times, number of outputs
         self._nt = len(self._times) - 1
@@ -149,7 +149,7 @@ class ARMA11LogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem):
-        super(ARMA11LogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times, number of outputs
         self._nt = len(self._times) - 2
@@ -205,7 +205,7 @@ class CauchyLogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem):
-        super(CauchyLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times, number of outputs
         self._nt = len(self._times)
@@ -326,7 +326,7 @@ class CensoredGaussianLogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem, lower=None, upper=None):
-        super(CensoredGaussianLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times, number of outputs
         self._nt = len(self._times)
@@ -617,8 +617,7 @@ class ConstantAndMultiplicativeGaussianLogLikelihood(
     """
 
     def __init__(self, problem):
-        super(ConstantAndMultiplicativeGaussianLogLikelihood, self).__init__(
-            problem)
+        super().__init__(problem)
 
         # Get number of times and number of noise parameters
         self._nt = len(self._times)
@@ -819,8 +818,7 @@ class GaussianIntegratedLogUniformLogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem):
-        super(GaussianIntegratedLogUniformLogLikelihood,
-              self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times, number of outputs
         self._nt = len(self._times)
@@ -908,7 +906,7 @@ class GaussianIntegratedUniformLogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem, lower, upper):
-        super(GaussianIntegratedUniformLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times, number of outputs
         self._nt = len(self._times)
@@ -1012,7 +1010,7 @@ class GaussianKnownSigmaLogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem, sigma):
-        super(GaussianKnownSigmaLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Store counts
         self._no = problem.n_outputs()
@@ -1110,7 +1108,7 @@ class GaussianLogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem):
-        super(GaussianLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times, number of outputs
         self._nt = len(self._times)
@@ -1169,7 +1167,7 @@ def __init__(self, problem, sigma):
         warnings.warn(
             'The class `pints.KnownNoiseLogLikelihood` is deprecated.'
             ' Please use `pints.GaussianKnownSigmaLogLikelihood` instead.')
-        super(KnownNoiseLogLikelihood, self).__init__(problem, sigma)
+        super().__init__(problem, sigma)
 
 
 class LogNormalLogLikelihood(pints.ProblemLogLikelihood):
@@ -1214,7 +1212,7 @@ class LogNormalLogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem, mean_adjust=False):
-        super(LogNormalLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times, number of outputs
         self._nt = len(self._times)
@@ -1352,7 +1350,7 @@ class MultiplicativeGaussianLogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem):
-        super(MultiplicativeGaussianLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times and number of outputs
         self._nt = len(self._times)
@@ -1411,13 +1409,11 @@ def __init__(self, log_likelihood):
         if not isinstance(log_likelihood, pints.ProblemLogLikelihood):
             raise ValueError(
                 'Given log_likelihood must extend pints.ProblemLogLikelihood')
-
-        # Call parent constructor
-        super(ScaledLogLikelihood, self).__init__(log_likelihood._problem)
-
-        # Store log-likelihood
         self._log_likelihood = log_likelihood
 
+        # Call this only after checking log_likelihood type
+        super().__init__(log_likelihood._problem)
+
         # Pre-calculate parts
         self._f = 1.0 / np.prod(self._values.shape)
 
@@ -1465,7 +1461,7 @@ class StudentTLogLikelihood(pints.ProblemLogLikelihood):
     """
 
     def __init__(self, problem):
-        super(StudentTLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
 
         # Get number of times, number of outputs
         self._nt = len(self._times)
@@ -1515,4 +1511,5 @@ def __init__(self, problem):
         warnings.warn(
             'The class `pints.UnknownNoiseLogLikelihood` is deprecated.'
             ' Please use `pints.GaussianLogLikelihood` instead.')
-        super(UnknownNoiseLogLikelihood, self).__init__(problem)
+        super().__init__(problem)
+

pints/_log_pdfs.py

@@ -182,7 +182,7 @@ class PooledLogPDF(LogPDF):
             pooled=[False, True])
     """
     def __init__(self, log_pdfs, pooled):
-        super(PooledLogPDF, self).__init__()
+        super().__init__()
 
         # Check input arguments
         if len(log_pdfs) < 2:
@@ -329,7 +329,7 @@ class ProblemLogLikelihood(LogPDF):
 
     """
     def __init__(self, problem):
-        super(ProblemLogLikelihood, self).__init__()
+        super().__init__()
         self._problem = problem
         # Cache some problem variables
         self._values = problem.values()
@@ -364,7 +364,7 @@ class LogPosterior(LogPDF):
         space.
     """
     def __init__(self, log_likelihood, log_prior):
-        super(LogPosterior, self).__init__()
+        super().__init__()
 
         # Check arguments
         if not isinstance(log_prior, LogPrior):
@@ -453,7 +453,7 @@ class SumOfIndependentLogPDFs(LogPDF):
         ])
     """
     def __init__(self, log_likelihoods):
-        super(SumOfIndependentLogPDFs, self).__init__()
+        super().__init__()
 
         # Check input arguments
         if len(log_likelihoods) < 2:

pints/_log_priors.py

@@ -1053,7 +1053,7 @@ def __init__(self, mean, standard_deviation):
         warnings.warn(
             'The class `pints.NormalLogPrior` is deprecated.'
             ' Please use `pints.GaussianLogPrior` instead.')
-        super(NormalLogPrior, self).__init__(mean, standard_deviation)
+        super().__init__(mean, standard_deviation)
 
 
 class StudentTLogPrior(pints.LogPrior):

pints/_logger.py

@@ -33,7 +33,7 @@ class Logger(object):
 
     """
     def __init__(self):
-        super(Logger, self).__init__()
+        super().__init__()
 
         # Log to screen
         self._stream = sys.stdout

pints/_mcmc/__init__.py

@@ -18,6 +18,7 @@ class MCMCSampler(pints.Loggable, pints.TunableMethod):
     :class:`pints.TunableMethod` interfaces.
     """
 
+    @classmethod
     def name(self):
         """
         Returns this method's full name.
@@ -1128,8 +1129,7 @@ def __init__(self, log_pdf, chains, x0, sigma0=None, transformation=None,
         warnings.warn(
             'The class `pints.MCMCSampling` is deprecated.'
             ' Please use `pints.MCMCController` instead.')
-        super(MCMCSampling, self).__init__(log_pdf, chains, x0, sigma0,
-                                           transformation, method=method)
+        super().__init__(log_pdf, chains, x0, sigma0, transformation, method)
 
 
 def mcmc_sample(log_pdf, chains, x0, sigma0=None, transformation=None,

pints/_mcmc/_adaptive_covariance.py

@@ -27,7 +27,7 @@ class AdaptiveCovarianceMC(pints.SingleChainMCMC):
     """
 
     def __init__(self, x0, sigma0=None):
-        super(AdaptiveCovarianceMC, self).__init__(x0, sigma0)
+        super().__init__(x0, sigma0)
 
         # Current running status, used to initialise on first run and check
         # that certain methods are only called before or during run.